Data Science VS. Data Mining, What’s the Difference?

The amount of data we generate is more than ever before as we progress through the digital age. We’ve created more than 90% of the world’s data in the last two years alone, and the growth rate is exponential.   

It’s fair to argue that data influences almost every aspect of life. And in such a world, being able to categorize and analyze data effectively is a critical element of company success.   

That is why everyone must distinguish these two phrases. The meanings of the words science and mining are entirely opposite and distinct in their own terms.   

However, when the prefix ‘data’ is placed before them, they establish a strong link. Many find it challenging to distinguish between the two. You’ve come to the right place if you can’t identify the difference between the two. This post will explain the fundamental distinctions between data science and data mining.   

Data Science VS. Data Mining

Meaning 

What is Data Science? 

Data Science is an emerging branch of computer science that concentrates on data. There has been a lot of buzz about “data science” in the media, yet there aren’t many definitions for the most fundamental terms.   

What exactly is Data Science? Data Science is a multidisciplinary field. It extracts valuable information from both unstructured and structured data using a collection of tools, algorithms, and machine concepts.   

Data science is a field involving data analysis and modeling to comprehend the complicated world of data. It is not merely statistics or machine learning. A data scientist oversees this task; he gathers data from many sources, organizes and analyzes it. And then communicates the data to impact business operations. The aim is to get functional insights out of data.   

What is Data Mining? 

Data mining is extracting valuable data from massive collections of raw data gathered regularly by finding anomalies, patterns, and correlations.   

It just converts a large amount of unstructured data into knowledge. It is connected to machine learning and is defined as the science of retrieving valuable data from massive data collections or databases.   

Data mining may be used as a data analysis method to find solutions in various fields. It may be seen as a consequence of information technology’s natural progression.    

Data mining aims to solve complicated computer problems. It uncovers previously undiscovered aspects of current data and identifying statistical patterns or trends.    

Top 10 Data Science Trends for 2022

Steps 

Data Science: 

• Data collection is the initial phase in the process. It might either be structured, unstructured, or semi-structured. 
• Now that you have acquired it, it is time to work on your data. It is cleaned and turned into an understandable format to obtain the maximum value from the ‘raw’ data. 

• Now that the data has been cleaned, it’s time to evaluate it using algorithms and statistical models. 
• When dealing with large volumes of data, creating visuals or graphs is the most effective approach to examining and sharing results. 
• Machine learning algorithms help in the discovery of latest information and the forecasting of future trends. This stage can help you create new goods and processes in addition to making forecasts. 
• Insights support the development of new features that help to improve model outputs and give fast and accurate results.   

Data Mining: 

• The first step in data mining is gathering and consolidating data from multiple sources. 
• We choose only the data suitable for data mining in this stage since not all the data collected is valid. 
• The data you have picked may include errors, missing values, or inconsistencies that need to be cleaned up. 
• Smoothing, aggregation, normalizing, etc., procedures are used to convert data into a format that can be understood. 
• Now is the moment to use data mining techniques such as clustering and association analysis to identify exciting trends. 
• To minimize misunderstanding, repetitive patterns should be removed, and the remaining patterns should be analyzed. 
• The final phase in this strategy is to make appropriate judgments based on the knowledge gained during the process.   

Goal 

Data mining is a technique for converting unstructured data into useful information. It aims to solve complicated computer issues. It does so by uncovering previously undiscovered aspects of current data and identifying statistical laws or patterns.   

Data Science is a field in and of itself, not only statistics or machine learning. It aims to employ specialized computational approaches to find relevant and usable information within a dataset so that you can make critical decisions.   

Field 

Database systems, data analysis, data engineering, visualization, experimentation, predictive modeling, and business intelligence are all fields that fall under the umbrella term data science. Data science is a broad term that encompasses a variety of methods, applications, and disciplines.   

On the other hand, data mining is the process of extracting useful information from massive volumes of data and transforming that information into structured knowledge.    

Data mining is a subset of the more extensive KDD process. Whereas data science is a collection of methods and practices that may or may not involve data mining.   

 Applications 

Data Science: 

Healthcare: The use of data science solutions in various industries is rapidly expanding. Healthcare is one of the primary industries increasingly revolutionized by data science.   

Internet Search: Many search engines, such as Yahoo, Google, and Bing, employ data science algorithms to offer the best results for our search query in under a second.   

Fraud and Risk Detection: Data science allows massive data to be used in innovative, scientific, and exploratory ways. The information is gathered at random from numerous industries and platforms, such as phone polls, emails, and social media sites.   

Image Recognition: In this digital age, data science technologies have begun to recognize the human face in all images stored in their database.   

Data Mining: 

Market Analysis: A market analysis gives a plethora of data to contribute to the development of your marketing strategy. Market size statistics might help you assess whether or not a market is worth investing in. But you also need to understand how the market operates.   

Financial Analysis: The banking and finance system is based on reliable and high-quality data. Data on finances and users may be used for various purposes in lending departments, including establishing credit ratings.   

Higher Education: As the need for higher education grows throughout the world, institutions seek various solutions to meet the growing demand. Educational institutions use data mining to determine whether students would be interested in a particular program and who would require further guidance.    

Detecting Fraud: The techniques used to detect fraudulent activity took a long time to work out. Fraud detection has gotten easier since the emergence of data mining. Data mining has made it simpler to spot patterns and take precautions to protect the privacy of users’ data.   

Conclusion: 

When dealing with the ever-increasing volume of data, data science and data mining are critical tools for organizations to uncover opportunities and make informed decisions.   

While the goal of all these areas remain the same – to draw insights that might help a business expand. The important distinctions are in the tools and technology involved, the type of work, and the processes taken to accomplish that goal.   

SG Analytics can help you acquire and manage relevant data and obtain insights on how to make better business decisions. Contact us right now to learn more about our data science consulting services.